Circular trace transform and its PCA‐based fusion features for image representation. Issue 10 (1st October 2018)

• Record Type: Journal Article
• Authors: Wang, Yuling; Li, Ming; Zhong, Guoyun; Li, Junhua; Lu, Yuming
• Is Part Of: IET image processing. Volume 12:Issue 10(2018)
• Page Start: 1797
• Page End: 1806
• Publication Date: 2018-10-01
• Subjects: image representation -- image texture -- principal component analysis -- feature extraction -- image fusion; PCA‐based fusion features -- arc‐shaped textures -- circular TT -- image function -- quadruple CTT features -- TT features -- FFCT_PCA -- mixed texture images -- texture properties -- principle component analysis -- complementary descriptor -- deeper intrinsic information -- image representation -- circular trace transform; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.1146
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : To improve the image representation efficiency of trace transform (TT) features to images with circular and arc‐shaped textures, the authors propose circular TT (CTT) to extract features. CTT consists of tracing an image with circles around which certain functionals of the image function are calculated. Quadruple CTT features can be generated through three successive functionals in the results of CTT, while different quadruple features can be obtained by choosing different combinations of successive functionals. These quadruple features can represent different texture properties and deeper intrinsic information of an image. By fusing CTT features and TT features based on PCA (FFCT_PCA), they construct a new complementary descriptor with much less dimension, further improving the representation performance for mixed texture images. Experimental results demonstrate that CTT has better performance than TT in recognising images with circular and arc‐shaped textures, and FFCT_PCA has the potential to outperform the state‐of‐the‐art feature extraction methods.